





"""simple docstring"""


import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


__magic_name__       :					int						=			1e-4

if is_torch_available():
		import torch

		from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
		from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder






@require_torch
class 							lowercase__			:

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A=1_6						,   _A=1_3						,   _A=7						,   _A=1_4						,   _A=1_0						,   _A=1_9						,   _A=5						,   _A=4						,   _A=True						,   _A=1_6						,   _A=2						,   _A=4						,   _A=4						,   _A="gelu"						,   _A=0.1						,   _A=0.1						,   _A=[1, 2, 3, 4, 5]						,   _A=2_5						,   _A=5						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			d_model
									UpperCamelCase				:		Tuple				   =			parent
									UpperCamelCase				:		Optional[Any]				   =			batch_size
									UpperCamelCase				:		Union[str, Any]				   =			prediction_length
									UpperCamelCase				:		List[Any]				   =			context_length
									UpperCamelCase				:		int				   =			cardinality
									UpperCamelCase				:		Tuple				   =			num_time_features
									UpperCamelCase				:		List[Any]				   =			lags_sequence
									UpperCamelCase				:		Optional[Any]				   =			embedding_dimension
									UpperCamelCase				:		Any				   =			is_training
									UpperCamelCase				:		Any				   =			hidden_size
									UpperCamelCase				:		List[str]				   =			num_hidden_layers
									UpperCamelCase				:		Optional[Any]				   =			num_attention_heads
									UpperCamelCase				:		Any				   =			intermediate_size
									UpperCamelCase				:		Tuple				   =			hidden_act
									UpperCamelCase				:		Union[str, Any]				   =			hidden_dropout_prob
									UpperCamelCase				:		Union[str, Any]				   =			attention_probs_dropout_prob

									UpperCamelCase				:		Union[str, Any]				   =			context_length
									UpperCamelCase				:		str				   =			prediction_length + label_length
									UpperCamelCase				:		Dict				   =			label_length

									UpperCamelCase				:		Any				   =			moving_average
									UpperCamelCase				:		int				   =			autocorrelation_factor







			def   _a      (      self    ):






									'''simple docstring'''







									return AutoformerConfig(
									    d_model=self.d_model						,   encoder_layers=self.num_hidden_layers						,   decoder_layers=self.num_hidden_layers						,   encoder_attention_heads=self.num_attention_heads						,   decoder_attention_heads=self.num_attention_heads						,   encoder_ffn_dim=self.intermediate_size						,   decoder_ffn_dim=self.intermediate_size						,   dropout=self.hidden_dropout_prob						,   attention_dropout=self.attention_probs_dropout_prob						,   prediction_length=self.prediction_length						,   context_length=self.context_length						,   label_length=self.label_length						,   lags_sequence=self.lags_sequence						,   num_time_features=self.num_time_features						,   num_static_categorical_features=1						,   cardinality=[self.cardinality]						,   embedding_dimension=[self.embedding_dimension]						,   moving_average=self.moving_average						,   )







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			config.context_length + max(config.lags_sequence    )

									UpperCamelCase				:		Any				   =			ids_tensor([self.batch_size, 1]						,   config.cardinality[0]    )
									UpperCamelCase				:		Any				   =			floats_tensor([self.batch_size, _past_length, config.num_time_features]    )
									UpperCamelCase				:		Union[str, Any]				   =			floats_tensor([self.batch_size, _past_length]    )
									UpperCamelCase				:		str				   =			floats_tensor([self.batch_size, _past_length]    ) > 0.5

									# decoder inputs
									UpperCamelCase				:		int				   =			floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]    )
									UpperCamelCase				:		Optional[Any]				   =			floats_tensor([self.batch_size, config.prediction_length]    )

									UpperCamelCase				:		Dict				   =			{
									    """past_values""": past_values,
									    """static_categorical_features""": static_categorical_features,
									    """past_time_features""": past_time_features,
									    """past_observed_mask""": past_observed_mask,
									    """future_time_features""": future_time_features,
									    """future_values""": future_values,
									}
									return inputs_dict







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			self.get_config()
									UpperCamelCase				:		int				   =			self.prepare_autoformer_inputs_dict(_A    )
									return config, inputs_dict







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			self.prepare_config_and_inputs()
									return config, inputs_dict







			def   _a      (      self						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			AutoformerModel(config=_A    ).to(_A    ).eval()
									UpperCamelCase				:		List[str]				   =			model(**_A    )

									UpperCamelCase				:		Any				   =			outputs.encoder_last_hidden_state
									UpperCamelCase				:		List[Any]				   =			outputs.last_hidden_state

									with tempfile.TemporaryDirectory() as tmpdirname:
															UpperCamelCase				:		Optional[Any]				   =			model.get_encoder()
															encoder.save_pretrained(_A    )
															UpperCamelCase				:		Union[str, Any]				   =			AutoformerEncoder.from_pretrained(_A    ).to(_A    )

									UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase				:		Dict				   =			model.create_network_inputs(**_A    )
									UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]    )

									UpperCamelCase				:		Dict				   =			torch.cat(
									    (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])						,   dim=-1						,   )
									UpperCamelCase				:		str				   =			encoder(inputs_embeds=_A    )[0]
									self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3    )

									UpperCamelCase				:		Tuple				   =			(
									    torch.mean(transformer_inputs[:, : config.context_length, ...]						,   dim=1    )
									    .unsqueeze(1    )
									    .repeat(1						,   config.prediction_length						,   1    )
									)
									UpperCamelCase				:		Optional[Any]				   =			torch.zeros(
									    [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]						,   device=enc_input.device						,   )

									UpperCamelCase				:		List[Any]				   =			torch.cat(
									    (
									        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)						,   dim=1    ),
									        feature[:, config.context_length - config.label_length :, ...],
									    )						,   dim=-1						,   )
									UpperCamelCase				:		str				   =			torch.cat(
									    (
									        torch.cat((trend_input[:, -config.label_length :, ...], mean)						,   dim=1    ),
									        feature[:, config.context_length - config.label_length :, ...],
									    )						,   dim=-1						,   )

									with tempfile.TemporaryDirectory() as tmpdirname:
															UpperCamelCase				:		Any				   =			model.get_decoder()
															decoder.save_pretrained(_A    )
															UpperCamelCase				:		Optional[Any]				   =			AutoformerDecoder.from_pretrained(_A    ).to(_A    )

									UpperCamelCase				:		str				   =			decoder(
									    trend=_A						,   inputs_embeds=_A						,   encoder_hidden_states=_A						,   )[0]

									self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3    )






@require_torch
class 							lowercase__			(   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE					,     unittest.TestCase	):

			"""simple docstring"""
			__lowerCAmelCase     :				Tuple										=  (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
			__lowerCAmelCase     :				str										=  (AutoformerForPrediction,) if is_torch_available() else ()
			__lowerCAmelCase     :				Dict										=  {"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
			__lowerCAmelCase     :				List[Any]										=  False
			__lowerCAmelCase     :				Any										=  False
			__lowerCAmelCase     :				List[Any]										=  False
			__lowerCAmelCase     :				Optional[int]										=  False
			__lowerCAmelCase     :				Optional[Any]										=  False
			__lowerCAmelCase     :				Optional[int]										=  False







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			AutoformerModelTester(self    )
									UpperCamelCase				:		int				   =			ConfigTester(self						,   config_class=_A						,   has_text_modality=_A    )







			def   _a      (      self    ):






									'''simple docstring'''







									self.config_tester.run_common_tests()







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
															UpperCamelCase				:		Optional[int]				   =			model_class(_A    )

															with tempfile.TemporaryDirectory() as tmpdirname:
																					model.save_pretrained(_A    )
																					UpperCamelCase   ,						UpperCamelCase				:		Optional[Any]				   =			model_class.from_pretrained(_A						,   output_loading_info=_A    )
															self.assertEqual(info["""missing_keys"""]						,   []    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_encoder_decoder_model_standalone(*_A    )







			@unittest.skip(reason="""Model has no tokens embeddings"""    )
			def   _a      (      self    ):






									'''simple docstring'''







									pass







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			inspect.signature(getattr(_A						,   """forward"""    )    )
									# The main input is the name of the argument after `self`
									UpperCamelCase				:		Optional[Any]				   =			list(model_signature.parameters.keys()    )[1]
									self.assertEqual(AutoformerModel.main_input_name						,   _A    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															UpperCamelCase				:		Dict				   =			model_class(_A    )
															UpperCamelCase				:		int				   =			inspect.signature(model.forward    )
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															UpperCamelCase				:		str				   =			[*signature.parameters.keys()]

															UpperCamelCase				:		Optional[Any]				   =			[
															    """past_values""",
															    """past_time_features""",
															    """past_observed_mask""",
															    """static_categorical_features""",
															    """static_real_features""",
															    """future_values""",
															    """future_time_features""",
															]

															if model.__class__.__name__ in ["AutoformerForPrediction"]:
																					expected_arg_names.append("""future_observed_mask"""    )

															expected_arg_names.extend(
															    [
															        """decoder_attention_mask""",
															        """head_mask""",
															        """decoder_head_mask""",
															        """cross_attn_head_mask""",
															        """encoder_outputs""",
															        """past_key_values""",
															        """output_hidden_states""",
															        """output_attentions""",
															        """use_cache""",
															        """return_dict""",
															    ]    )

															self.assertListEqual(arg_names[: len(_A    )]						,   _A    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase   ,						UpperCamelCase				:		List[str]				   =			self.model_tester.prepare_config_and_inputs_for_common()
									UpperCamelCase				:		Dict				   =			True

									UpperCamelCase				:		Tuple				   =			getattr(self.model_tester						,   """seq_length"""						,   _A    )
									UpperCamelCase				:		Dict				   =			getattr(self.model_tester						,   """decoder_seq_length"""						,   _A    )
									UpperCamelCase				:		Optional[Any]				   =			getattr(self.model_tester						,   """encoder_seq_length"""						,   _A    )
									UpperCamelCase				:		Optional[int]				   =			getattr(self.model_tester						,   """d_model"""						,   _A    )
									UpperCamelCase				:		Optional[Any]				   =			getattr(self.model_tester						,   """num_attention_heads"""						,   _A    )
									UpperCamelCase				:		Tuple				   =			d_model // num_attention_heads

									for model_class in self.all_model_classes:
															UpperCamelCase				:		List[Any]				   =			True
															UpperCamelCase				:		Any				   =			False
															UpperCamelCase				:		List[str]				   =			True
															UpperCamelCase				:		List[str]				   =			model_class(_A    )
															model.to(_A    )
															model.eval()
															with torch.no_grad():
																					UpperCamelCase				:		Tuple				   =			model(**self._prepare_for_class(_A						,   _A    )    )
															UpperCamelCase				:		Optional[Any]				   =			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
															self.assertEqual(len(_A    )						,   self.model_tester.num_hidden_layers    )

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															UpperCamelCase				:		Tuple				   =			True
															UpperCamelCase				:		Optional[int]				   =			model_class(_A    )
															model.to(_A    )
															model.eval()
															with torch.no_grad():
																					UpperCamelCase				:		List[Any]				   =			model(**self._prepare_for_class(_A						,   _A    )    )
															UpperCamelCase				:		List[str]				   =			outputs.encoder_attentions
															self.assertEqual(len(_A    )						,   self.model_tester.num_hidden_layers    )

															self.assertListEqual(
															    list(attentions[0].shape[-3:]    )						,   [self.model_tester.num_attention_heads, encoder_seq_length, dim]						,   )
															UpperCamelCase				:		List[str]				   =			len(_A    )

															UpperCamelCase				:		List[str]				   =			7

															if "last_hidden_state" in outputs:
																					correct_outlen += 1

															if "trend" in outputs:
																					correct_outlen += 1

															if "past_key_values" in outputs:
																					correct_outlen += 1  # past_key_values have been returned

															if "loss" in outputs:
																					correct_outlen += 1

															if "params" in outputs:
																					correct_outlen += 1

															self.assertEqual(_A						,   _A    )

															# decoder attentions
															UpperCamelCase				:		Optional[int]				   =			outputs.decoder_attentions
															self.assertIsInstance(_A						,   (list, tuple)    )
															self.assertEqual(len(_A    )						,   self.model_tester.num_hidden_layers    )
															self.assertListEqual(
															    list(decoder_attentions[0].shape[-3:]    )						,   [self.model_tester.num_attention_heads, decoder_seq_length, dim]						,   )

															# cross attentions
															UpperCamelCase				:		int				   =			outputs.cross_attentions
															self.assertIsInstance(_A						,   (list, tuple)    )
															self.assertEqual(len(_A    )						,   self.model_tester.num_hidden_layers    )
															self.assertListEqual(
															    list(cross_attentions[0].shape[-3:]    )						,   [self.model_tester.num_attention_heads, decoder_seq_length, dim]						,   )

									# Check attention is always last and order is fine
									UpperCamelCase				:		Any				   =			True
									UpperCamelCase				:		Dict				   =			True
									UpperCamelCase				:		int				   =			model_class(_A    )
									model.to(_A    )
									model.eval()
									with torch.no_grad():
															UpperCamelCase				:		int				   =			model(**self._prepare_for_class(_A						,   _A    )    )

									self.assertEqual(out_len + 2						,   len(_A    )    )

									UpperCamelCase				:		str				   =			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

									self.assertEqual(len(_A    )						,   self.model_tester.num_hidden_layers    )
									self.assertListEqual(
									    list(self_attentions[0].shape[-3:]    )						,   [self.model_tester.num_attention_heads, encoder_seq_length, dim]						,   )







			@is_flaky()
			def   _a      (      self    ):






									'''simple docstring'''







									super().test_retain_grad_hidden_states_attentions()


def      UpperCamelCase							(SCREAMING_SNAKE_CASE="train-batch.pt"     ):
						UpperCamelCase				:		List[str]				   =			hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch"""							,     filename=SCREAMING_SNAKE_CASE							,     repo_type="""dataset"""     )
						UpperCamelCase				:		Optional[int]				   =			torch.load(SCREAMING_SNAKE_CASE							,     map_location=SCREAMING_SNAKE_CASE     )
						return batch






@require_torch
@slow
class 							lowercase__			(   unittest.TestCase	):

			"""simple docstring"""







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly"""    ).to(_A    )
									UpperCamelCase				:		List[str]				   =			prepare_batch()

									with torch.no_grad():
															UpperCamelCase				:		Dict				   =			model(
															    past_values=batch["""past_values"""]						,   past_time_features=batch["""past_time_features"""]						,   past_observed_mask=batch["""past_observed_mask"""]						,   static_categorical_features=batch["""static_categorical_features"""]						,   future_values=batch["""future_values"""]						,   future_time_features=batch["""future_time_features"""]						,   )[0]

									UpperCamelCase				:		Optional[int]				   =			torch.Size(
									    (6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size)    )
									self.assertEqual(output.shape						,   _A    )

									UpperCamelCase				:		Optional[int]				   =			torch.tensor(
									    [[0.35_93, -1.33_98, 0.63_30], [0.22_79, 1.53_96, -0.17_92], [0.04_50, 1.32_25, -0.23_35]]						,   device=_A    )
									self.assertTrue(torch.allclose(output[0, :3, :3]						,   _A						,   atol=_A    )    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""    ).to(_A    )
									UpperCamelCase				:		Dict				   =			prepare_batch("""val-batch.pt"""    )
									with torch.no_grad():
															UpperCamelCase				:		Optional[Any]				   =			model(
															    past_values=batch["""past_values"""]						,   past_time_features=batch["""past_time_features"""]						,   past_observed_mask=batch["""past_observed_mask"""]						,   static_categorical_features=batch["""static_categorical_features"""]						,   ).encoder_last_hidden_state
									UpperCamelCase				:		Dict				   =			torch.Size((6_4, model.config.context_length, model.config.d_model)    )
									self.assertEqual(output.shape						,   _A    )

									UpperCamelCase				:		Any				   =			torch.tensor(
									    [[-0.07_34, -0.90_36, 0.83_58], [4.71_86, 2.41_13, 1.95_81], [1.79_53, 2.35_58, 1.29_70]]						,   device=_A    )
									self.assertTrue(torch.allclose(output[0, :3, :3]						,   _A						,   atol=_A    )    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""    ).to(_A    )
									UpperCamelCase				:		List[Any]				   =			prepare_batch("""val-batch.pt"""    )
									with torch.no_grad():
															UpperCamelCase				:		Optional[Any]				   =			model.generate(
															    static_categorical_features=batch["""static_categorical_features"""]						,   past_time_features=batch["""past_time_features"""]						,   past_values=batch["""past_values"""]						,   future_time_features=batch["""future_time_features"""]						,   past_observed_mask=batch["""past_observed_mask"""]						,   )
									UpperCamelCase				:		Tuple				   =			torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length)    )
									self.assertEqual(outputs.sequences.shape						,   _A    )

									UpperCamelCase				:		Dict				   =			torch.tensor([31_30.67_63, 40_56.52_93, 70_53.07_86]						,   device=_A    )
									UpperCamelCase				:		Optional[Any]				   =			outputs.sequences.mean(dim=1    )
									self.assertTrue(torch.allclose(mean_prediction[0, -3:]						,   _A						,   rtol=1e-1    )    )











"""simple docstring"""


import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests


def        UpperCAmelCase      (					a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :str						=			None
						if token is not None:
												lowerCAmelCase   :List[Any]						=			{'Accept': 'application/vnd.github+json', 'Authorization': F"""Bearer {token}"""}

						lowerCAmelCase   :Dict						=			F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
						lowerCAmelCase   :Optional[Any]						=			requests.get(a__     ,      headers=a__ ).json()
						lowerCAmelCase   :Tuple						=			{}

						try:
												job_links.update({job['name']: job['html_url'] for job in result['jobs']} )
												lowerCAmelCase   :List[Any]						=			math.ceil((result['total_count'] - 1_00) / 1_00 )

												for i in range(a__ ):
																		lowerCAmelCase   :List[Any]						=			requests.get(url + F"""&page={i + 2}"""     ,      headers=a__ ).json()
																		job_links.update({job['name']: job['html_url'] for job in result['jobs']} )

												return job_links
						except Exception:
												print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )

						return {}


def        UpperCAmelCase      (					a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[Any]						=			None
						if token is not None:
												lowerCAmelCase   :Any						=			{'Accept': 'application/vnd.github+json', 'Authorization': F"""Bearer {token}"""}

						lowerCAmelCase   :Optional[int]						=			F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
						lowerCAmelCase   :Any						=			requests.get(a__     ,      headers=a__ ).json()
						lowerCAmelCase   :str						=			{}

						try:
												artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )
												lowerCAmelCase   :List[Any]						=			math.ceil((result['total_count'] - 1_00) / 1_00 )

												for i in range(a__ ):
																		lowerCAmelCase   :List[Any]						=			requests.get(url + F"""&page={i + 2}"""     ,      headers=a__ ).json()
																		artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )

												return artifacts
						except Exception:
												print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )

						return {}


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[Any]						=			None
						if token is not None:
												lowerCAmelCase   :Optional[Any]						=			{'Accept': 'application/vnd.github+json', 'Authorization': F"""Bearer {token}"""}

						lowerCAmelCase   :Tuple						=			requests.get(a__     ,      headers=a__     ,      allow_redirects=a__ )
						lowerCAmelCase   :Optional[int]						=			result.headers['Location']
						lowerCAmelCase   :int						=			requests.get(a__     ,      allow_redirects=a__ )
						lowerCAmelCase   :Union[str, Any]						=			os.path.join(a__     ,      F"""{artifact_name}.zip""" )
						with open(a__     ,      'wb' ) as fp:
												fp.write(response.content )


def        UpperCAmelCase      (					a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[Any]						=			[]
						lowerCAmelCase   :Dict						=			[]
						lowerCAmelCase   :Optional[Any]						=			None

						with zipfile.ZipFile(a__ ) as z:
												for filename in z.namelist():
																		if not os.path.isdir(a__ ):
																								# read the file
																								if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
																														with z.open(a__ ) as f:
																																				for line in f:
																																										lowerCAmelCase   :int						=			line.decode('UTF-8' ).strip()
																																										if filename == "failures_line.txt":
																																																try:
																																																						# `error_line` is the place where `error` occurs
																																																						lowerCAmelCase   :int						=			line[: line.index(': ' )]
																																																						lowerCAmelCase   :int						=			line[line.index(': ' ) + len(': ' ) :]
																																																						errors.append([error_line, error] )
																																																except Exception:
																																																						# skip un-related lines
																																																						pass
																																										elif filename == "summary_short.txt" and line.startswith('FAILED ' ):
																																																# `test` is the test method that failed
																																																lowerCAmelCase   :int						=			line[len('FAILED ' ) :]
																																																failed_tests.append(a__ )
																																										elif filename == "job_name.txt":
																																																lowerCAmelCase   :List[str]						=			line

						if len(a__ ) != len(a__ ):
												raise ValueError(
												    F"""`errors` and `failed_tests` should have the same number of elements. Got {len(a__ )} for `errors` """
												    F"""and {len(a__ )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
												    ' problem.' )

						lowerCAmelCase   :Optional[int]						=			None
						if job_name and job_links:
												lowerCAmelCase   :Dict						=			job_links.get(a__     ,      a__ )

						# A list with elements of the form (line of error, error, failed test)
						lowerCAmelCase   :Union[str, Any]						=			[x + [y] + [job_link] for x, y in zip(a__     ,      a__ )]

						return result


def        UpperCAmelCase      (					a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :Any						=			[]

						lowerCAmelCase   :Optional[int]						=			[os.path.join(a__     ,      a__ ) for p in os.listdir(a__ ) if p.endswith('.zip' )]
						for p in paths:
												errors.extend(get_errors_from_single_artifact(a__     ,      job_links=a__ ) )

						return errors


def        UpperCAmelCase      (					a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :int						=			Counter()
						counter.update([x[1] for x in logs] )
						lowerCAmelCase   :Tuple						=			counter.most_common()
						lowerCAmelCase   :Dict						=			{}
						for error, count in counts:
												if error_filter is None or error not in error_filter:
																		lowerCAmelCase   :Optional[int]						=			{'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

						lowerCAmelCase   :Optional[Any]						=			dict(sorted(r.items()     ,      key=lambda a__ : item[1]["count"]     ,      reverse=a__ ) )
						return r


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			test.split('::' )[0]
						if test.startswith('tests/models/' ):
												lowerCAmelCase   :Union[str, Any]						=			test.split('/' )[2]
						else:
												lowerCAmelCase   :Optional[int]						=			None

						return test


def        UpperCAmelCase      (					a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :str						=			[(x[0], x[1], get_model(x[2] )) for x in logs]
						lowerCAmelCase   :Any						=			[x for x in logs if x[2] is not None]
						lowerCAmelCase   :Tuple						=			{x[2] for x in logs}

						lowerCAmelCase   :Optional[Any]						=			{}
						for test in tests:
												lowerCAmelCase   :Union[str, Any]						=			Counter()
												# count by errors in `test`
												counter.update([x[1] for x in logs if x[2] == test] )
												lowerCAmelCase   :List[str]						=			counter.most_common()
												lowerCAmelCase   :str						=			{error: count for error, count in counts if (error_filter is None or error not in error_filter)}
												lowerCAmelCase   :Optional[Any]						=			sum(error_counts.values() )
												if n_errors > 0:
																		lowerCAmelCase   :Dict						=			{'count': n_errors, 'errors': error_counts}

						lowerCAmelCase   :Dict						=			dict(sorted(r.items()     ,      key=lambda a__ : item[1]["count"]     ,      reverse=a__ ) )
						return r


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Any						=			'| no. | error | status |'
						lowerCAmelCase   :Optional[int]						=			'|-:|:-|:-|'
						lowerCAmelCase   :Tuple						=			[header, sep]
						for error in reduced_by_error:
												lowerCAmelCase   :Optional[Any]						=			reduced_by_error[error]['count']
												lowerCAmelCase   :List[str]						=			F"""| {count} | {error[:1_00]} |  |"""
												lines.append(a__ )

						return "\n".join(a__ )


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Any						=			'| model | no. of errors | major error | count |'
						lowerCAmelCase   :int						=			'|-:|-:|-:|-:|'
						lowerCAmelCase   :int						=			[header, sep]
						for model in reduced_by_model:
												lowerCAmelCase   :Dict						=			reduced_by_model[model]['count']
												lowerCAmelCase	, lowerCAmelCase   :Any						=			list(reduced_by_model[model]['errors'].items() )[0]
												lowerCAmelCase   :Any						=			F"""| {model} | {count} | {error[:60]} | {_count} |"""
												lines.append(a__ )

						return "\n".join(a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
		parser.add_argument(
		    '--output_dir',
		    type=str,
		    required=True,
		    help='Where to store the downloaded artifacts and other result files.',
		)
		parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()

		os.makedirs(args.output_dir, exist_ok=True)

		__SCREAMING_SNAKE_CASE   =   get_job_links(args.workflow_run_id, token=args.token)
		__SCREAMING_SNAKE_CASE   =   {}
		# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
		# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
		if _job_links:
				for k, v in _job_links.items():
						# This is how GitHub actions combine job names.
						if " / " in k:
								__SCREAMING_SNAKE_CASE   =   k.find(' / ')
								__SCREAMING_SNAKE_CASE   =   k[index + len(' / ') :]
						__SCREAMING_SNAKE_CASE   =   v
		with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp:
				json.dump(job_links, fp, ensure_ascii=False, indent=4)

		__SCREAMING_SNAKE_CASE   =   get_artifacts_links(args.workflow_run_id, token=args.token)
		with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
				json.dump(artifacts, fp, ensure_ascii=False, indent=4)

		for idx, (name, url) in enumerate(artifacts.items()):
				download_artifact(name, url, args.output_dir, args.token)
				# Be gentle to GitHub
				time.sleep(1)

		__SCREAMING_SNAKE_CASE   =   get_all_errors(args.output_dir, job_links=job_links)

		# `e[1]` is the error
		__SCREAMING_SNAKE_CASE   =   Counter()
		counter.update([e[1] for e in errors])

		# print the top 30 most common test errors
		__SCREAMING_SNAKE_CASE   =   counter.most_common(30)
		for item in most_common:
				print(item)

		with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp:
				json.dump(errors, fp, ensure_ascii=False, indent=4)

		__SCREAMING_SNAKE_CASE   =   reduce_by_error(errors)
		__SCREAMING_SNAKE_CASE   =   reduce_by_model(errors)

		__SCREAMING_SNAKE_CASE   =   make_github_table(reduced_by_error)
		__SCREAMING_SNAKE_CASE   =   make_github_table_per_model(reduced_by_model)

		with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp:
				fp.write(sa)
		with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp:
				fp.write(sa)